Deep learning has elevated the performance of speech enhancement in the past decade [1]}.
Since the very first success of deep learning in offline enhancement [2]}, there have been growing interests in using DNNs for low-latency speech enhancement, as many application scenarios require online real-time processing.
For example, the recent deep noise suppression challenges [3]} target at speech enhancement in a monaural teleconferencing setup, requiring a processing latency less than 40 ms on a specified Intel i5 processor.
Similar latency requirements exist in other related challenges [4]}, [5]}. The recent Clarity challenge [6]} aims at multi-microphone speech enhancement in a hearing aid setup, requiring an algorithmic latency of at maximum 5 ms.
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